Investigating The Bidirectional Relationship Between Low Back Pain, Hip Pain, Knee Pain, And Gait Abnormalities: A Mendelian Randomization Study.

Gait abnormalities are associated with altered lower-limb biomechanics and functional impairment in musculoskeletal disorders. Although low back pain, hip pain, and knee pain have been linked to impaired gait in observational studies, their causal relationships remain unclear. We conducted a bidirectional Mendelian randomization (MR) study to assess potential causal associations between site-specific pain and gait abnormalities. Independent single nucleotide polymorphisms from large-scale genome-wide association studies were used as genetic instruments for low back pain, hip pain, and knee pain. Gait abnormalities, defined in the source GWAS as self-reported difficulty walking, were used as the outcome in forward MR analyses and as the exposure in reverse MR analyses. The inverse-variance weighted (IVW) method was the primary analysis, supported by sensitivity analyses for heterogeneity, horizontal pleiotropy, and robustness. In forward analyses, genetically predicted low back pain (OR = 1.53, 95% CI = 1.058-2.219, P = 0.024) and hip pain (OR = 1.55, 95% CI = 1.067-2.252, P = 0.021) were associated with increased risk of gait abnormalities in IVW analyses. However, the four additional MR methods were not statistically significant, although estimates were generally directionally consistent. Genetically predicted knee pain showed no significant association (OR = 2.15, 95% CI = 0.234-19.789, P = 0.498), with substantial uncertainty reflected by the wide confidence interval. Reverse MR analyses found no evidence that genetic liability to gait abnormalities increased the risk of low back, hip, or knee pain, although only four gait-abnormality SNPs were available. No substantial heterogeneity or horizontal pleiotropy was detected. Overall, this study provides limited IVW-based genetic evidence that low back pain and hip pain may be associated with gait abnormalities. Findings should be interpreted cautiously and validated using larger GWAS datasets with refined phenotypes.